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Namaste!

My name is Midhun. I'm currently working at Synopsys Inc..

  • šŸ”­ My forte is in DevOps, MLOps and Cloud
  • šŸŒŸ I'm currently working on Enterprise system migrations, Developer Tools, Kubeflow, AWS, Deployment automation, Kubernetes, Kafka, ...
  • šŸ‘Øā€šŸ’» Iā€™m looking to collaborate on new project ideas and code reviews
  • šŸ¤” Iā€™m looking to collaborate with like-minded peeps on the Development and Operations side of things
  • šŸ’¬ Ask me about System integrations, Architecture, Kubernetes, Automation, Linux, ...
  • šŸ˜„ Pronouns: He/him
  • āš” Fun fact: I'm multilingual
A random meme šŸ˜†

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Midhun Nair's Projects

ansible icon ansible

Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.

bankit-app icon bankit-app

This project was part of TCS Case study and was created in Python using Flask.

brainbot icon brainbot

A simple and fun Ryver bot to liven up the conversation

chat-app icon chat-app

A chat application programmed in java with GUI.

community icon community

Information about the Kubeflow community including proposals and governance information.

container-images icon container-images

This repo contains container images that I use time-to-time to bring up stuff or to debug

internal-acls icon internal-acls

Repository used to main group ACLs used by Kubeflow developers

katib icon katib

Repository for hyperparameter tuning

kibble icon kibble

Apache Kibble - a tool to collect, aggregate and visualize data about any software project

kubeflow icon kubeflow

Machine Learning Toolkit for Kubernetes

machine-learning-project-walkthrough icon machine-learning-project-walkthrough

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

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